{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import gc\n",
    "import logging\n",
    "from abc import abstractmethod\n",
    "from collections import defaultdict\n",
    "from copy import deepcopy\n",
    "from typing import Dict, List, Literal, Optional, Tuple, Union, cast\n",
    "\n",
    "import lightning as L\n",
    "import matplotlib as mpl\n",
    "import matplotlib.font_manager\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.utils.hooks\n",
    "from accelerate import init_empty_weights\n",
    "from torch import Tensor, nn\n",
    "from tqdm.auto import tqdm\n",
    "from transformers import AutoModelForCausalLM, LlamaForCausalLM\n",
    "from typing_extensions import override\n",
    "\n",
    "from fusion_bench.method import BaseModelFusionAlgorithm\n",
    "from fusion_bench.method.pruning.prune_utils import (\n",
    "    PruningType,\n",
    "    compute_sparsity,\n",
    "    find_linear_layers,\n",
    "    semistructured_magnitude_prune_,\n",
    "    unstructured_magnitude_prune_,\n",
    ")\n",
    "from fusion_bench.method.pruning.wanda_utils.data import get_loaders\n",
    "from fusion_bench.method.pruning.wanda_utils.prune import prepare_calibration_input\n",
    "from fusion_bench.mixins import SimpleProfilerMixin\n",
    "from fusion_bench.modelpool import CausalLMPool\n",
    "from fusion_bench.utils import cache_to_disk, print_parameters, timeit_context\n",
    "from fusion_bench.utils.devices import get_device\n",
    "\n",
    "# use times-new roman, no type 3 fonts\n",
    "plt.rcParams[\"pdf.fonttype\"] = 42\n",
    "plt.rcParams[\"ps.fonttype\"] = 42\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "# math font\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"cm\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(model_path):\n",
    "    model = AutoModelForCausalLM.from_pretrained(\n",
    "        model_path,\n",
    "        torch_dtype=torch.float16,\n",
    "    )\n",
    "    return model\n",
    "\n",
    "\n",
    "def iterative_weight_update(W, S, mask, rank):\n",
    "    L = W - S\n",
    "    u, sigma, vh = torch.linalg.svd(L.float(), full_matrices=False)\n",
    "    v = vh.t()\n",
    "    rank = min(sigma.size(0) - 1, rank)\n",
    "    uk = u[:, rank:]\n",
    "    sk = sigma[rank:]\n",
    "    vk = v[:, rank:]\n",
    "    S = S + (mask * (uk @ torch.diag(sk) @ vk.t())).to(S.dtype)\n",
    "    spectrum_ratio = torch.sum(sigma[:rank] * sigma[:rank]) / torch.sum(sigma * sigma)\n",
    "    return (S, spectrum_ratio)\n",
    "\n",
    "\n",
    "def pcp_loss_with_mask(w, q, mask):\n",
    "    _lambda = 1 / np.sqrt(np.max(w.size()))\n",
    "    nuclear_loss = torch.linalg.matrix_norm((w * (~mask) + q * mask).float(), ord=\"nuc\")\n",
    "    l1_loss = _lambda * torch.linalg.matrix_norm((w * mask - q * mask).float(), ord=1)\n",
    "    return nuclear_loss + l1_loss\n",
    "\n",
    "\n",
    "def PCP_search_with_mask(w, mask, T_max=1000, lr=1e-2):\n",
    "    q = torch.zeros_like(w).float().requires_grad_(True)\n",
    "    optimizer = torch.optim.AdamW([q], lr=lr)\n",
    "    lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, T_max=T_max, eta_min=1e-1 * lr\n",
    "    )\n",
    "    for step_idx in tqdm(range(T_max)):\n",
    "        optimizer.zero_grad()\n",
    "        loss = pcp_loss_with_mask(w, q, mask)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        if step_idx % (T_max // 20) == 0:\n",
    "            print(f\"Step {step_idx}: Loss {loss.item()}\")\n",
    "    s = w * mask - q * mask\n",
    "    return s\n",
    "\n",
    "\n",
    "def random_matrix_with_rank_k(m, n, k):\n",
    "    u = torch.randn(m, k)\n",
    "    v = torch.randn(n, k)\n",
    "    return u @ v.t() #/ np.sqrt(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_singluar_values(rank, sparsity_ratio, target_rank, m=512, n=512, T_max=100):\n",
    "    W = random_matrix_with_rank_k(m, n, rank).to(\"cuda:4\")\n",
    "    S = unstructured_magnitude_prune_(\n",
    "        W.clone(), torch.abs, sparsity_ratio=sparsity_ratio\n",
    "    )\n",
    "    mask = S != 0\n",
    "    s_t = S.clone()\n",
    "    for step_idx, r_t in tqdm(\n",
    "        enumerate(np.linspace(2, target_rank, T_max, dtype=np.int64))\n",
    "    ):\n",
    "        s_t, spectrum_ratio = iterative_weight_update(W, s_t, mask, rank=r_t)\n",
    "    return torch.linalg.svdvals(W - s_t.float())"
   ]
  },
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   "source": [
    "data = defaultdict(list)\n",
    "\n",
    "for _ in range(5):\n",
    "    for w_rank in [512]:\n",
    "        for sparsity_ratio in [0.3, 0.5, 0.7]:\n",
    "            W = random_matrix_with_rank_k(512, 512, w_rank).to(\"cuda:4\")\n",
    "            S = unstructured_magnitude_prune_(\n",
    "                W.clone(), torch.abs, sparsity_ratio=sparsity_ratio\n",
    "            )\n",
    "            L = W - S\n",
    "            singular_values = torch.linalg.svdvals(L.float()).cpu().numpy()\n",
    "            for i, sigma in enumerate(singular_values):\n",
    "                data[\"label\"].append(\n",
    "                    R\"$\\boldsymbol{S}=\\boldsymbol{W}\\odot\\boldsymbol{P}$\"\n",
    "                )\n",
    "                data[\"w_rank\"].append(w_rank)\n",
    "                data[\"sparsity_ratio\"].append(sparsity_ratio)\n",
    "                data[\"index\"].append(i)\n",
    "                data[\"singular_value\"].append(sigma)\n",
    "\n",
    "    for w_rank in [512]:\n",
    "        for sparsity_ratio in [0.3, 0.5, 0.7]:\n",
    "            singular_values = (\n",
    "                get_singluar_values(w_rank, sparsity_ratio, target_rank=512)\n",
    "                .cpu()\n",
    "                .numpy()\n",
    "            )\n",
    "            for i, sigma in enumerate(singular_values):\n",
    "                data[\"label\"].append(R\"Ours\")\n",
    "                data[\"w_rank\"].append(w_rank)\n",
    "                data[\"sparsity_ratio\"].append(sparsity_ratio)\n",
    "                data[\"index\"].append(i)\n",
    "                data[\"singular_value\"].append(sigma)\n",
    "\n",
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1310407/262247531.py:20: UserWarning: The palette list has more values (9) than needed (3), which may not be intended.\n",
      "  sns.lineplot(\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\n",
    "    \"#7EA6E0\",\n",
    "    \"#EA6B66\",\n",
    "    \"#97D077\",\n",
    "    \"#FFB570\",\n",
    "    \"#FF99FF\",\n",
    "    \"#CCCC00\",\n",
    "    \"#000099\",\n",
    "    \"#666666\",\n",
    "    \"#CCCCCC\",\n",
    "]\n",
    "\n",
    "# set font size to 16\n",
    "plt.rcParams.update({\"font.size\": 14})\n",
    "# set the font size of title to 16\n",
    "plt.rcParams.update({\"axes.titlesize\": 15})\n",
    "\n",
    "plt.figure(figsize=(5.5, 4.5))\n",
    "\n",
    "sns.lineplot(\n",
    "    data=data[data[\"w_rank\"] == 512],\n",
    "    x=\"index\",\n",
    "    y=\"singular_value\",\n",
    "    hue=\"sparsity_ratio\",\n",
    "    style=\"label\",\n",
    "    palette=colors,\n",
    ")\n",
    "\n",
    "# plot vertical lines at 0.3 * 512, 0.5 * 512, 0.7 * 512\n",
    "# as well text labels\n",
    "plt.axvline(x=0.3 * 512, color=\"gray\", alpha=0.5)\n",
    "plt.axvline(x=0.5 * 512, color=\"gray\", alpha=0.5)\n",
    "plt.axvline(x=0.7 * 512, color=\"gray\", alpha=0.5)\n",
    "plt.text(0.3 * 512 - 20, 835, \"$i=0.3r$\", rotation=90, verticalalignment=\"center\")\n",
    "plt.text(0.5 * 512 - 20, 835, \"$i=0.5r$\", rotation=90, verticalalignment=\"center\")\n",
    "plt.text(0.7 * 512 - 20, 835, \"$i=0.7r$\", rotation=90, verticalalignment=\"center\")\n",
    "\n",
    "# change the pos of legend to center right\n",
    "plt.legend(loc=\"center right\", fontsize=8)\n",
    "\n",
    "legend = plt.legend(loc=\"center right\")\n",
    "for text in legend.get_texts():\n",
    "    if text.get_text() == \"sparsity_ratio\":\n",
    "        text.set_text(\"Sparsity Ratio\")\n",
    "    elif text.get_text() == \"label\":\n",
    "        text.set_text(\"Method\")\n",
    "\n",
    "plt.xlabel(\"Singular Value Index\")\n",
    "plt.ylabel(\"Singular Value Magnitude\")\n",
    "plt.title(\"Spectrum of Singular Values\")\n",
    "\n",
    "plt.savefig(\"spectrum.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e91d7cac431c4a2da2a801979ed89740",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d4f6eae6bea41b2b5ea74b1c24d05d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30a59a2ce5db46689d4b93549eb9ce51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b23a3851a478456ca44faf584cbbac84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7c817ca37e147bca1a364aea011b75e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83e1e2fb38ac43069161350dc0be8910",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1dfd01eb077f42619409092a70e43b90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8bef4a0bc844aeda813c12cc92b826a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c3adf6266334bce934c4891ff70b0e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "478e5ca32d884e1b997a849227537787",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad079f2c2f7b4860906d142a4c0a03db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f5db48924d547caaed52c083a6a19e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b505ebfceb774dadacb9758f4dde42ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a10a414f883541c3948ed26a9a2c44f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7275836ac5a5492da51b7cc47e95b5c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30557d191e2146aba754d33aeb4524f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0ae6872cecb4b819ca4248d3c30156a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c67da87858d649b88980866f54ecb756",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24be7c05c71a424cadee85013d3d89db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec76ea0c348f4c3fb0f54b8f8ff93c8c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = defaultdict(list)\n",
    "\n",
    "for _ in range(5):\n",
    "    for w_rank in [64, 128, 256, 512]:\n",
    "        for sparsity_ratio in [0.5]:\n",
    "            W = random_matrix_with_rank_k(512, 512, w_rank).to(\"cuda:4\")\n",
    "            S = unstructured_magnitude_prune_(\n",
    "                W.clone(), torch.abs, sparsity_ratio=sparsity_ratio\n",
    "            )\n",
    "            L = W - S\n",
    "            singular_values = torch.linalg.svdvals(L.float()).cpu().numpy()\n",
    "            for i, sigma in enumerate(singular_values):\n",
    "                data[\"label\"].append(\n",
    "                    R\"$\\boldsymbol{S}=\\boldsymbol{W}\\odot\\boldsymbol{P}$\"\n",
    "                )\n",
    "                data[\"w_rank\"].append(w_rank)\n",
    "                data[\"sparsity_ratio\"].append(sparsity_ratio)\n",
    "                data[\"index\"].append(i)\n",
    "                data[\"singular_value\"].append(sigma)\n",
    "\n",
    "    for w_rank in [64, 128, 256, 512]:\n",
    "        for sparsity_ratio in [0.5]:\n",
    "            singular_values = (\n",
    "                get_singluar_values(w_rank, sparsity_ratio, target_rank=512)\n",
    "                .cpu()\n",
    "                .numpy()\n",
    "            )\n",
    "            for i, sigma in enumerate(singular_values):\n",
    "                data[\"label\"].append(R\"Ours\")\n",
    "                data[\"w_rank\"].append(w_rank)\n",
    "                data[\"sparsity_ratio\"].append(sparsity_ratio)\n",
    "                data[\"index\"].append(i)\n",
    "                data[\"singular_value\"].append(sigma)\n",
    "\n",
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1310407/688813921.py:20: UserWarning: The palette list has more values (9) than needed (4), which may not be intended.\n",
      "  sns.lineplot(\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\n",
    "    \"#7EA6E0\",\n",
    "    \"#EA6B66\",\n",
    "    \"#97D077\",\n",
    "    \"#FFB570\",\n",
    "    \"#FF99FF\",\n",
    "    \"#CCCC00\",\n",
    "    \"#000099\",\n",
    "    \"#666666\",\n",
    "    \"#CCCCCC\",\n",
    "]\n",
    "\n",
    "# set font size to 16\n",
    "plt.rcParams.update({\"font.size\": 14})\n",
    "# set the font size of title to 16\n",
    "plt.rcParams.update({\"axes.titlesize\": 15})\n",
    "\n",
    "plt.figure(figsize=(5.5, 4.5))\n",
    "\n",
    "sns.lineplot(\n",
    "    data=data,\n",
    "    x=\"index\",\n",
    "    y=\"singular_value\",\n",
    "    hue=\"w_rank\",\n",
    "    style=\"label\",\n",
    "    palette=colors,\n",
    ")\n",
    "\n",
    "# plot vertical lines at 0.3 * 512, 0.5 * 512, 0.7 * 512\n",
    "# as well text labels\n",
    "# plt.axvline(x=0.3 * 512, color=\"gray\", alpha=0.5)\n",
    "plt.axvline(x=0.5 * 512, color=\"gray\", alpha=0.5)\n",
    "# plt.axvline(x=0.7 * 512, color=\"gray\", alpha=0.5)\n",
    "# plt.text(0.3 * 512 - 20, 32.5, \"$x=0.3$\", rotation=90, verticalalignment=\"center\")\n",
    "plt.text(0.5 * 512 - 20, 600, \"$i=256$\", rotation=90, verticalalignment=\"center\")\n",
    "# plt.text(0.7 * 512 - 20, 32.5, \"$x=0.7$\", rotation=90, verticalalignment=\"center\")\n",
    "\n",
    "# change the pos of legend to center right\n",
    "plt.legend(loc=\"center right\", fontsize=8)\n",
    "\n",
    "legend = plt.legend(loc=\"center right\")\n",
    "for text in legend.get_texts():\n",
    "    if text.get_text() == \"w_rank\":\n",
    "        text.set_text(R\"$\\text{rank}(\\boldsymbol{W})$\")\n",
    "    elif text.get_text() == \"label\":\n",
    "        text.set_text(\"Method\")\n",
    "\n",
    "plt.xlabel(\"Singular Value Index\")\n",
    "plt.ylabel(\"Singular Value Magnitude\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(\"Spectrum of Singular Values\")\n",
    "\n",
    "plt.savefig(\"spectrum_wrank.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fusionbench",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
